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Alfian Ma'arif
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International Journal of Robotics and Control Systems
Published by Association for Scientific Computing Electronics and Engineering
ISSN : -     EISSN : 27752658     DOI : https://doi.org/10.31763/ijrcs
Core Subject : Engineering,
Control & Systems Engineering Electrical & Electronics Engineering
International Journal of Robotics and Control Systems is open access and peer-reviewed international journal that invited academicians (students and lecturers), researchers, scientists, and engineers to exchange and disseminate their work, development, and contribution in the area of robotics and control technology systems experts. Its scope includes Industrial Robots, Humanoid Robot, Flying Robot, Mobile Robot, Proportional-Integral-Derivative (PID) Controller, Feedback Control, Linear Control (Compensator, State Feedback, Servo State Feedback, Observer, etc.), Nonlinear Control (Feedback Linearization, Sliding Mode Controller, Backstepping, etc.), Robust Control, Adaptive Control (Model Reference Adaptive Control, etc.), Geometry Control, Intelligent Control (Fuzzy Logic Controller (FLC), Neural Network Control), Power Electronic Control, Artificial Intelligence, Embedded Systems, Internet of Things (IoT) in Control and Robot, Network Control System, Controller Optimization (Linear Quadratic Regulator (LQR), Coefficient Diagram Method, Metaheuristic Algorithm, etc.), Modelling and Identification System.
Arjuna Subject : Teknik (semua kategori) - Teknik Kontrol dan Sistem
Articles 35 Documents
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Search results for , issue "Vol 5, No 1 (2025)" : 35 Documents clear
Design of a PID Speed Controller for BLDC Motor with Cascaded Boost Converter for High-Efficiency Industrial Applications Al-Dabbagh, Zainab Ameer; Shneen, Salam Waley
International Journal of Robotics and Control Systems Vol 5, No 1 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i1.1601

Abstract

Achieving high voltage and efficiency in brushless direct current (BLDC) motor applications is challenging, particularly in industrial settings where precise speed control is essential. This study addresses this issue by designing a cascaded boost converter with a Proportional–integral–derivative (PID) speed controller. The cascaded boost converter is first simulated in an open-loop circuit using MATLAB/SIMULINK, followed by integrating the BLDC motor and adding a PID controller to achieve precise speed control. The PID controller achieved a steady-state speed of 1500 rad/s with an input voltage of 15 volts, resulting in an output voltage of over 50 volts. The efficiency of the system was improved by 87.87% compared to traditional methods. While the PID controller effectively controls the motor speed, it may consume more power and require more complex tuning in certain operating conditions. The proposed system is suitable for high-voltage industrial applications, such as electric vehicle drives and renewable energy systems, where precise speed control and high efficiency are critical.  The PID controller is user-friendly and easy to implement, making it suitable for various industrial applications. The system was tested under varying load conditions and input voltages to ensure robust performance and reliability. Future work will optimize the PID controller for real-time applications and integrate advanced control strategies to enhance system performance. A cascaded boost converter is a type of DC-DC converter that boosts the input voltage to a higher level, while a PID controller is a control loop feedback mechanism widely used for precise control of dynamic systems.
Two-Flexible-Link Manipulator Vibration Reduction Through Fuzzy-Based Position Faris, Waleed F.; Rabie, M.; Moaaz, Ahmad O.; Ghazaly, Nouby M.; Makrahy, Mostafa M.
International Journal of Robotics and Control Systems Vol 5, No 1 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i1.1669

Abstract

The increasing demand for robotic applications has emphasized the need for advanced control strategies, particularly for flexible manipulators with lightweight links. These manipulators offer advantages such as reduced energy consumption, increased payload capacity, and precise high-speed operation but face challenges due to oscillations and delays caused by their flexibility. This study evaluates the performance of Fuzzy Logic Control (FLC) and Linear Quadratic Regulator (LQR) techniques for a Quanser two-link flexible manipulator, using quantitative metrics to compare their effectiveness. The LQR controller was implemented using state-space modeling, with weighting matrices Q and R tuned to achieve minimal overshoot and fast settling times. The FLC system employed five triangular membership functions for inputs and outputs, covering normalized ranges of [-1, 1] for angular errors and [-2.75, 2.75] for error rates, with a heuristic rule base designed to optimize performance. Simulations were conducted under step input conditions at target angles of 30° and 60°, with performance evaluated using vibration amplitude, settling time, steady-state error, and overshoot. Quantitatively, the LQR controller reduced vibration amplitudes to 5 radians for a 30° input and achieved settling times of approximately 2 seconds. For the same conditions, the FLC system reduced vibrations further to 4 radians, though with slightly longer settling times of around 2.3 seconds. At a 60° input, LQR vibrations peaked at over 10 radians, while FLC maintained peak vibrations at approximately 4 radians. These results highlight the FLC’s superior vibration suppression, particularly at higher input angles, albeit with marginally slower response times. However, the study was limited to idealized simulation conditions and requires further experimental validation. This research underscores the trade-offs between LQR’s precision and FLC’s adaptability, emphasizing the importance of parameter tuning and system modeling in achieving optimal performance for flexible manipulators.
Detection of Sealing Defects in Canned Sardines Using Local Binary Pattern and Perceptron Techniques for Enhanced Quality Control Mansour, Salah-Eddine; Sakhi, Abdelhak
International Journal of Robotics and Control Systems Vol 5, No 1 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i1.1737

Abstract

In the canned sardine production industry, sealing issues often arise due to various factors, such as the quantity of fish in the can or improper calibration of the sealing machine. These sealing defects can result in poorly sealed cans that may explode and contaminate an entire production batch, leading to significant financial losses and damage to the company's reputation. This study proposes an advanced and reliable method for classifying fish can images to detect potential defects, such as sealing issues, which are critical to maintaining quality standards in the canning industry. Our classification method utilizes the Local Binary Patterns (LBP) algorithm for feature extraction across the entire dataset of images. The extracted features are then processed using a Perceptron classifier to identify poorly sealed cans. This approach achieved a precision score of 0.85, demonstrating its effectiveness. Additionally, our analysis revealed that LBP significantly contributes to improving classification accuracy. By automating and enhancing the quality assurance process, this method provides the canning industry with a robust tool for ensuring high product standards, minimizing errors, and increasing efficiency in production lines.
Comparative Analysis of Yolov-8 Segmentation for Gait Performance in Individuals with Lower Limb Disabilities Wulanningrum, Resty; Handayani, Anik Nur; Herwanto, Heru Wahyu
International Journal of Robotics and Control Systems Vol 5, No 1 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i1.1731

Abstract

This research aims to develop an example of gait pattern segmentation between normal and disabled individuals. Walking is the movement of moving from one place to another, where individuals with physical limitations on the legs have different walking patterns compared to individuals without physical limitations. This study classifies gait into three categories, namely individuals with assistive devices (crutches), individuals without assistive devices, and normal individuals. The study involved 10 subjects, consisting of 2 individuals with assistive devices, 3 individuals without assistive devices, and 5 normal individuals. The research process was conducted through three main stages, namely: image database creation, data annotation, and model training and segmentation using YOLOv8. YOLOv8-seg is the platform used to segment the data. The test results showed that the YOLOv8L-seg model achieved convergence value at the 23rd epoch with the 4th scenario in recognizing the walking patterns of the three categories. However, research on walking patterns of people with disabilities faces several obstacles, such as the lack of confidence or emotion of the subject during the data collection process, which is conducted at the location of the subject's choice. In addition, YOLOv8-seg showed consistent performance across the five models used, obtaining a maximum mAP50 value of 0.995 for mAP50 box and mAP50 mask.
A Novel Hybrid Backstepping and Fuzzy Control for Three Phase Induction Motor Drivers Pham, Ngoc Thuy; Nguyen, Phu Diep
International Journal of Robotics and Control Systems Vol 5, No 1 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i1.1707

Abstract

High-performance control using three-phase Induction Motors (IM) is increasingly required in industrial applications. However, due to the nonlinear structure and the continuous impact of issues such as load disturbances and motor parameter variations, traditional control techniques cannot achieve the desired high-performance drive system. In this paper, a new hybrid control scheme combining Backstepping (BS) with fuzzy logic (FL) control for the outer speed control loop to enhancing Field Oriented Control (FOC) vector control performance of the SPIM drives, is proposed. Different from the BS control strategies that have been proposed in the control of IM drive systems before, this paper proposes to use FL control theory to continuously update the coefficients appearing in the virtual control vectors extracted from the traditional BS control technique according to the input error of the system. This contributes to improving the performance of the drive system, enhancing the stability and adaptability of the drive system. Lyapunov stability theory is used to design the drive system to ensure the stability of the overall system. The proposed speed control strategy is validated through Matlab-Simulink. The simulation results show that: first, the proposed control strategy provides fast speed response, and the convergence capability of the drive system remains in an optimal state during transient modes without causing overshoot. Second, the drive system operates stably over the long term under load disturbances.
Wavelet Analysis- Singular Value Decomposition Based Method for Precise Fault Localization in Power Distribution Networks Using k-NN Classifier Raj, Abhishek; Mishra, Chandra Sekhar; Joga, S Ramana Kumar; Elzein, I. M.; Mohanty, Asit; lika, Sneha; Mahmoud, Mohamed Metwally; Ewais, Ahmed Mostafa
International Journal of Robotics and Control Systems Vol 5, No 1 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i1.1543

Abstract

This article presents a wavelet analysis-singular value decomposition (WA-SVD) based method for precise fault localization in recent power distribution networks using k-NN Classifier. The WA-SVD leverages the slime mould algorithm (SMA) and graph theory (GT) in enhancing the overall accuracy of fault localization. To validate the proposed methodology, extensive tests are conducted on various benchmark systems, including the IEEE 33-bus radial distribution system, the IEEE 33-bus meshed loop unbalanced distribution system, the IEEE 33-bus system with integrated renewable energy sources, and the IEEE 13-bus feeder test system. The results demonstrate a high fault classification accuracy of 99.08%, with an average localization error of just 1.2% of the total line length. The k-NN classifier exhibited a precision of 98.2% and a recall of 99.2%, underscoring the reliability and sensitivity of the proposed method. Additionally, the computational efficiency of the algorithm is evidenced by an average processing time of 0.0764 seconds per fault event, making it well-suited for real-time applications.
Fractional Approach to Two-Group Neutron Diffusion in Slab Reactors Batiha, Iqbal M.; Allouch, Nadia; Shqair, Mohammed; Jebril, Iqbal H.; Alkhazaleh, Shawkat; Momani, Shaher
International Journal of Robotics and Control Systems Vol 5, No 1 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i1.1524

Abstract

The two-energy neutron diffusion model in slab reactors characterizes neutron behavior across two energy groups: fast and thermal. Fast neutrons, generated by fission, decelerate through collisions, transitioning into thermal neutrons. This model employs diffusion equations to compute neutron flux distributions and reactor parameters, thereby optimizing reactor design and safety to ensure efficient neutron utilization and stable, sustained nuclear reactions. The primary objective of this research is to explore both analytical and numerical solutions to the two-energy neutron diffusion model in slab reactors. Specifically, we will utilize the Laplace transform method for an analytical solution of the two-energy neutron diffusion model. Subsequently, employing the Caputo differentiator, we transform the original neutron diffusion model into its fractional-order equivalents, yielding the fractional-order two-energy group neutron diffusion model in slab reactors. To address the resulting fractional-order system, we develop a novel approach aimed at reducing the 2β-order system to a β-order system, where β ∈ (0, 1]. This transformed system is then solved using the Modified Fractional Euler Method (MFEM), an advanced variation of the fractional Euler method. Finally, we present numerical simulations that validate our results and demonstrate their applicability.
Autonomous Driving Model with Collision Prediction for Urban and Extra-Urban Environments Hafid, Yassine El; Ligabi, Tarik; Zahraoui, Yassine
International Journal of Robotics and Control Systems Vol 5, No 1 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i1.1681

Abstract

This study introduces an architecture for an autonomous vehicle control system based on a collision detector and geometric modeling of trajectories. The goal is to develop a robust and reliable control model that can navigate metropolitan environments, often crowded with pedestrians and bicycles, as well as suburban areas, where traffic patterns can fluctuate. We have created a modular control unit that includes a collision predictor, which interacts closely with the decision module. The executed algorithm demonstrates the effectiveness of our system by ensuring the safety and comfort of the passengers. It can identify potential collisions from a distance and initiate braking preventively, following precise guidelines for deceleration and acceleration. To validate our methods, we are looking at simulations of realistic case studies. The research conducted underscores a crucial advancement in the development of safer and more flexible autonomous driving technologies.
Neural Network Architectures for UAV Path Planning: A Comparative Study with A* Algorithm as Benchmark Airlangga, Gregorius; Bata, Julius; Nugroho, Oskar Ika Adi; Sugianto, Lai Ferry
International Journal of Robotics and Control Systems Vol 5, No 1 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i1.1757

Abstract

Autonomous path planning for Unmanned Aerial Vehicles (UAVs) plays a critical role in applications ranging from disaster response to urban logistics. Traditional algorithms, such as A*, are widely recognized for their reliability in generating collision-free and efficient trajectories but often struggle with scalability in complex and dynamic environments. This study evaluates the performance of several neural network architectures, including MLP-LSTM, CNN-GRU, CNN-LSTM, CNN BILSTM, and others, as potential alternatives to classical methods. A dataset of trajectories generated by the A* algorithm was used to train and benchmark the models, enabling direct performance comparison across key metrics such as path length, smoothness, clearance, collisions, and waypoint density. The results demonstrate that the MLP-LSTM model outperforms other neural architectures, producing paths that closely resemble A* trajectories with high smoothness and waypoint granularity. While some models, such as CNN-GRU and CNN-BILSTM, show promise in generating feasible paths, their performance is inconsistent across different UAV scenarios. Models like Residual CNN and Hybrid CNN-MHA failed to generate meaningful trajectories, highlighting the critical importance of architectural choices. This study underscores the potential of neural network models for UAV path planning.
Mitigating Subsynchronous Resonance in Doubly Fed Wind Turbine Induction Generator Using FACTS Devices: A Comparative Case Study Phuong, Bui Thi Hoa; Ngoc, Tran Thanh; Thanh, Pham Hong; Dai, Le Van
International Journal of Robotics and Control Systems Vol 5, No 1 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i1.1436

Abstract

Sub-synchronous resonance (SSR) may result from the recent integration of wind power generating systems (WPGS) based on double-fed induction generators (DFIG) into weak grids using long transmission lines with series capacitor adjustment. The amount of series compensation used in the transmission line determines how much SSR affects the grid, which may lead to serious instability. Flexible alternating current transmission system (FACTS) devices, which aid in controlling and stabilizing grid oscillations, are a workable way to lessen the impacts of SSR. In order to analyze the efficacy of FACTS controllers in mitigating SSR, this work examines the modeling and control techniques of WPGS-DFIG employing Thyristor controlled series capacitor (TCSC), static Var compensator (SVC), and static synchronous compensator (STATCOM). Time-domain simulations on a modified IEEE First benchmark, with varying series compensation levels and grid fault circumstances, are used to verify the study's correctness and effectiveness. According to the simulation findings, the STATCOM controller mitigates SSR far more effectively than TCSC and SVC. The STATCOM controller optimizes the performance of the WPGS-DFIG system by increasing dynamic responsiveness and grid stability in SSR-prone conditions.

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